Optical remote sensing is an important tool in the study of animal behavior
providing ecologists with the means to understand species–environment interactions
in combination with animal movement data. However, differences in
spatial and temporal resolution between movement and remote sensing data
limit their direct assimilation. In this context, we built a data-driven framework
to map resource suitability that addresses these differences as well as the limitations
of satellite imagery. It combines seasonal composites of multiyear surface
reflectances and optimized presence and absence samples acquired with animal
movement data within a cross-validation modeling scheme. Moreover, it
responds to dynamic, site-specific environmental conditions making it applicable
to contrasting landscapes. We tested this framework using five populations
of White Storks (Ciconia ciconia) to model resource suitability related to foraging
achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for
absences. These results were influenced by the temporal composition of the seasonal
reflectances indicated by the lower accuracies associated with higher day
differences in relation to the target dates. Additionally, population differences
in resource selection influenced our results marked by the negative relationship
between the model accuracies and the variability of the surface reflectances
associated with the presence samples. Our modeling approach spatially splits
presences between training and validation. As a result, when these represent different
and unique resources, we face a negative bias during validation. Despite
these inaccuracies, our framework offers an important basis to analyze species–
environment interactions. As it standardizes site-dependent behavioral and
environmental characteristics, it can be used in the comparison of intra- and
interspecies environmental requirements and improves the analysis of resource
selection along migratory paths. Moreover, due to its sensitivity to differences
in resource selection, our approach can contribute toward a better understanding
of species requirements.
%0 Journal Article
%1 noauthororeditor
%A Remelgado, Ruben
%A Leutner, Benjamin
%A Safi, Kamran
%A Sonnenschein, Ruth
%A Kuebert, Carina
%A Wegmann, Martin
%D 2017
%J Remote Sensing in Ecology and Conservation
%K article Wegmann Kuebert Leutner LSFE
%R 10.1002/rse2.70
%T Linking animal movement and remote sensing – mapping
resource suitability from a remote sensing perspective
%X Optical remote sensing is an important tool in the study of animal behavior
providing ecologists with the means to understand species–environment interactions
in combination with animal movement data. However, differences in
spatial and temporal resolution between movement and remote sensing data
limit their direct assimilation. In this context, we built a data-driven framework
to map resource suitability that addresses these differences as well as the limitations
of satellite imagery. It combines seasonal composites of multiyear surface
reflectances and optimized presence and absence samples acquired with animal
movement data within a cross-validation modeling scheme. Moreover, it
responds to dynamic, site-specific environmental conditions making it applicable
to contrasting landscapes. We tested this framework using five populations
of White Storks (Ciconia ciconia) to model resource suitability related to foraging
achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for
absences. These results were influenced by the temporal composition of the seasonal
reflectances indicated by the lower accuracies associated with higher day
differences in relation to the target dates. Additionally, population differences
in resource selection influenced our results marked by the negative relationship
between the model accuracies and the variability of the surface reflectances
associated with the presence samples. Our modeling approach spatially splits
presences between training and validation. As a result, when these represent different
and unique resources, we face a negative bias during validation. Despite
these inaccuracies, our framework offers an important basis to analyze species–
environment interactions. As it standardizes site-dependent behavioral and
environmental characteristics, it can be used in the comparison of intra- and
interspecies environmental requirements and improves the analysis of resource
selection along migratory paths. Moreover, due to its sensitivity to differences
in resource selection, our approach can contribute toward a better understanding
of species requirements.
@article{noauthororeditor,
abstract = {Optical remote sensing is an important tool in the study of animal behavior
providing ecologists with the means to understand species–environment interactions
in combination with animal movement data. However, differences in
spatial and temporal resolution between movement and remote sensing data
limit their direct assimilation. In this context, we built a data-driven framework
to map resource suitability that addresses these differences as well as the limitations
of satellite imagery. It combines seasonal composites of multiyear surface
reflectances and optimized presence and absence samples acquired with animal
movement data within a cross-validation modeling scheme. Moreover, it
responds to dynamic, site-specific environmental conditions making it applicable
to contrasting landscapes. We tested this framework using five populations
of White Storks (Ciconia ciconia) to model resource suitability related to foraging
achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for
absences. These results were influenced by the temporal composition of the seasonal
reflectances indicated by the lower accuracies associated with higher day
differences in relation to the target dates. Additionally, population differences
in resource selection influenced our results marked by the negative relationship
between the model accuracies and the variability of the surface reflectances
associated with the presence samples. Our modeling approach spatially splits
presences between training and validation. As a result, when these represent different
and unique resources, we face a negative bias during validation. Despite
these inaccuracies, our framework offers an important basis to analyze species–
environment interactions. As it standardizes site-dependent behavioral and
environmental characteristics, it can be used in the comparison of intra- and
interspecies environmental requirements and improves the analysis of resource
selection along migratory paths. Moreover, due to its sensitivity to differences
in resource selection, our approach can contribute toward a better understanding
of species requirements.},
added-at = {2020-09-11T12:04:50.000+0200},
author = {Remelgado, Ruben and Leutner, Benjamin and Safi, Kamran and Sonnenschein, Ruth and Kuebert, Carina and Wegmann, Martin},
biburl = {https://www.bibsonomy.org/bibtex/2b53981a07936de2abeaaab99c52ac50a/earthobs_uniwue},
doi = {10.1002/rse2.70},
interhash = {3a580bf90c55cd944676429a01df71af},
intrahash = {b53981a07936de2abeaaab99c52ac50a},
journal = {Remote Sensing in Ecology and Conservation},
keywords = {article Wegmann Kuebert Leutner LSFE},
timestamp = {2020-11-18T22:08:33.000+0100},
title = {Linking animal movement and remote sensing – mapping
resource suitability from a remote sensing perspective},
year = 2017
}